Parallel Computing in the Computer Science Curriculum > Workshops > SIGCSE 2013

CSinParallel 1: Using Map-Reduce to Teach Parallel Programming Concepts Across the CS Curriculum

Part 1 -- Fundamentals; Introductory Courses

This first half of the workshop introduces map-reduce computing through the WebMapReduce (WMR)simplified interface to Hadoop, then shares our experience teaching map-reduce and related concepts of parallel and distributed computing to students in introductory sequences.

  1. Presentation (7:00pm)

  2. Hands-on exercises (7:30pm)

  3. Break (8:15pm)

Part 2 -- Intermediate and Advanced Courses

This part of the workshop uses WMR to explore use of map-reduce computing in more advanced courses, and examines the relationship between the WMR interface and the Hadoop computations it performs.

  1. Presentation (8:30pm)

  2. Hands-on exercises (9:00)

    • Assortment of WMR exercises

      Resources: Intro to WMR module; see WMR Activities Movie Data
      Data sets on HDFS: /shared/MovieLens2, /shared/gutenberg/CompleteShakespeare.txt, AnnaKarenina.txt, WarAndPeace.txt; /shared/gutenberg/all/group8; and code examples in various languages

  3. Discussion and feedback (9:40)

    Please complete our own short survey for grant assessment purposes.


Map-reduce, the cornerstone computational framework for cloud computing applications, has star appeal to draw students to the study of parallelism. Participants will carry out hands-on exercises designed for students at CS1/intermediate/advanced levels that introduce data-intensive scalable computing concepts, using WebMapReduce (WMR), a simplified open-source interface to the widely used Hadoop map-reduce programming environment. WMR supports programming in a choice of languages (including Java, Python, C++, C#, Scheme). Workshop includes brief introduction to direct Hadoop programming. Workshop materials will reside on, along with WMR software. Intended audience: CS instructors. Laptop required (Windows, Mac, or Linux).